Simplified model to predict the thermal demand profile of districts

Talebi, Behrang; Haghighat, Fariborz; Mirzaei, Parham A.

Abstract

Extensive research works have been carried out over the past few decades in the development of simulation tools to predict the thermal performance of buildings. These validated tools have been used in the design of the building and its components. However, limited simulation tools have been developed for modeling of district energy systems, which can potentially be a very laborious and time-consuming process. Besides many associated limitations, providing a realistic demand profile of the district energy systems is not a straightforward task due to high number of parameters involved in predicting a detail demand profile.
This paper reports the development of a simplified model for predicting the thermal demand profile of a district heating system. The paper describes the method used to develop two types of simplified models to predict the thermal load of a variety of buildings (residential, office, attached, detached, etc.). The predictions were also compared with those made by the detailed simulation models.
The simplified model was then utilized to predict the energy demand of a variety of districts types (residential, commercial or mix), and its prediction accuracy was compared with those made by detailed model: good agreement was observed between the results.

Citation

Talebi, B., Haghighat, F., & Mirzaei, P. A. (2017). Simplified model to predict the thermal demand profile of districts. Energy and Buildings, 145, https://doi.org/10.1016/j.enbuild.2017.03.062

• Journal Article Type: Article
• Acceptance Date: Mar 25, 2017
• Online Publication Date: Apr 7, 2017
• Publication Date: Jun 15, 2017
• Deposit Date: Jun 30, 2017
• Publicly Available Date: Jun 30, 2017
• Journal: Energy and Buildings
• Print ISSN: 0378-7788
• Electronic ISSN: 1872-6178
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 145
• DOI: https://doi.org/10.1016/j.enbuild.2017.03.062
• Keywords: Simulation tool; District system; Community; Demand profile
• Public URL: https://nottingham-repository.worktribe.com/output/866165
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0378778817302104
• Contract Date: Jun 30, 2017